Vehicle lane determination method, computer program product, and apparatus

ABSTRACT

Vehicle lane determination method in which radar data acquired at an ego vehicle is determined. Characteristics are extracted from the radar data associated with other vehicles, wherein the extracted characteristics include positional information for the other vehicles relative to the ego vehicle, and the step of extracting comprises selecting a road coordinate system and the positional information comprises a representation in the selected road coordinate system. The lane of the ego vehicle is determined based on the extracted characteristics meeting a set of lane determination conditions.

FIELD

The present disclosure relates to a vehicle lane determination method,computer program, and processing apparatus. The present disclosure isparticularly relevant to road lane determination methods and processingdevices for use in autonomous driving systems and ADAS (advanceddriver-assistance systems).

BACKGROUND

Modern vehicles typically include autonomous driving systems and/orADAS. Functions within these systems often need to determine theparticular road lane that the ego vehicle is currently traveling in. Forexample, the determined lane will have a bearing on vehicle navigationor vehicle lane centring functions. For instance, at complex highwayjunctions, the navigation system may need to specify to the driver whichlane to drive in. Similarly, autonomous driving systems need to knowwhich lane to follow.

Conventionally, a common technique of determining the current laneinvolves image processing a feed from a vehicle camera to identify thelane markings on a road. However, there are problems with thistechnique. Firstly, its reliability is reduced during bad weatherconditions or at night. Equally, on multiple lane highways, it can oftenbe difficult for the vehicle camera to see all the road lanes from thecamera's fixed position on the vehicle. At the same time, other vehiclescan block the camera's view of the lane markings, or markings can fadeover time. In some scenarios, it can also be difficult to differentiatelanes when there are unusual road layouts, such as when emergency lanesare present, or where the lane markings are misleading.

A further issue arises in that higher levels of autonomous drivingrequire a second channel, which uses a different sensor type, forplausibility checking.

To address the above limitations, it has been suggested to use radar byrepainting the lane markers with special radar reflective paint.However, this is exceptionally expensive to implement. Equally,techniques have been proposed where radar is used to determine thevehicle's distance to the road's guardrails, and this distance is usedto then estimate the number of lanes in between. However, this is notalways accurate, and is reliant on guardrails being present.

Accordingly, there remains a need to address the above shortcomings inconventional vehicle lane determination systems and methods.

SUMMARY

According to a first aspect, there is provided a vehicle lanedetermination method including the steps of: determining radar dataacquired at an ego vehicle; extracting characteristics from the radardata associated with other vehicles, wherein the extractedcharacteristics comprise positional information for the other vehiclesrelative to the ego vehicle, and wherein the step of extractingcomprises selecting a road coordinate system and the positionalinformation comprises a representation in the selected road coordinatesystem; and determining the lane of the ego vehicle based on theextracted characteristics meeting a set of lane determinationconditions.

In this way, a radar-based system is provided for lane determination,which utilises radar data containing information about surroundings andother vehicles, such as other cars, lorries, busses and motorbikes, toidentify the current lane of the ego vehicle. That is, the extractedcharacteristics from the radar data may correspond to parametersindicating other vehicles in adjacent road lanes to the ego vehicle. Atthe same time, information on the current road may be used to improvelane determination accuracy. For example, lane-splits or lane mergers,as well as the number of lanes can be used to interpret the positionalinformation of other cars and may be independent of ego-position of thevehicle. As such, the number and position of the other vehicles may beaccurately used to determine the number of lanes on one or both sides ofthe ego vehicle's current lane and thereby allow the ego-lane to bedetermined. That is, positional information providing the pose (i.e. theposition and orientation) of other detected vehicles can be used todetermine the ego vehicle's lane. Advantageously, this avoids the needfor expensive repainting of road markings or the presence of guiderailson roads in order to facilitate lane position detection. Equally, thelimitations of camera-based systems, such as low reliability in poorweather or at night, are avoided. The radar data may also be processedin conjunction with location and standard or high definition map data toprovide improved accuracy and/or plausibility determination.

In embodiments, the step of extracting characteristics from the receivedradar data comprises identifying other vehicles moving in a samelongitudinal direction to the ego vehicle. In this way, objects in radardata are identified semantically, and subsequent processing can beoptimised based on the location and movement of the identified othervehicles.

In embodiments, the vehicle lane determination method further includesthe step of filtering out static from the radar data. In this way, radardata arising from stationary objects, such as objects, such asguardrails, trees, bridges, etc., on the road area, may be filtered out.This filtering may be actioned before subsequent processing steps tominimise the amount of data to be processed.

In embodiments, the vehicle lane determination method further includesthe step of filtering out other vehicles moving in a differentlongitudinal direction to the ego vehicle. In this way, radar dataassociated with objects moving in the opposite direction, such asvehicles in the adjacent roadway traveling in the other direction, maybe filtered out.

In embodiments, the positional information comprises a representation ofthe other vehicles in a lateral direction relative to the ego vehicle.In this way, the lateral offset of other vehicles relative to the egovehicle may be determined.

In embodiments, the selected road coordinate system is selected toaccount for a location of the ego vehicle. In this way, the vehicles'current position may be used to improve lane determination accuracy. Assuch, the position of the ego-vehicle can be used to select the roadcoordinate system, such that its positional information improves lanedetermination. The location of the ego vehicle may be derived from mapin combination with GNSS/positional data or other sensor measurements.

In embodiments, the selected road coordinate system is selected toaccount for the current road characteristics. In this way,characteristics such as road layout or road curvature can be accountedfor when interpreting the radar data. In embodiments, the current roadcharacteristics may be obtained from a database or derived from vehiclesensor measurements. Accordingly, semantic information of the road (roadcharacteristics) can be used to select the road coordinate system, suchits semantic information improves lane determination and can be derivedeither from map in combination with GNSS/positional data or other sensormeasurements.

In embodiments, the extracted characteristics from the radar datafurther comprise at least one of the speed, heading, yaw rate,orientation, acceleration, positions, and size of the other vehicles. Inthis way, the determination accuracy may be improved by accounting formovement of the other vehicles.

In embodiments, the step of determining the lane of the ego vehiclefurther comprise accounting for at least one of the speed, heading, yawrate, orientation, acceleration and size of the ego vehicle. In thisway, a sensed motion of the ego vehicle may be used to improve thedetermination, for example by identify bends in the road or other roadfeatures.

In embodiments, the step of extracting characteristics includesclustering. In this way, clustering, such as K-means clustering, may beused to process radar data for identifying other vehicles.

In embodiments, the step of extracting characteristics comprisesaccounting for a determined minimum lateral distance between othervehicles. In this way, known characteristics about road lane spacing maybe used to improve lane determination accuracy.

In embodiments, the step of determining the lane of the ego vehiclecomprises using data from road coordinate system. In embodiments, thestep of determining the lane of the ego vehicle comprises accounting fora known number of lanes for the location of the ego vehicle determinedfrom road data. In this way, known information/characteristics about thenumber of road lanes may be used to improve lane determination accuracy.

In embodiments, the road data comprises a road map. In this way, lanedata from a standard or high definition road map may be utilised toprovide improved lane determination accuracy.

In embodiments, the vehicle lane determination method further includesthe step of verifying a plausibility for the determined lane of the egovehicle based on at least one of: (a) a known number of lanes for thelocation of the ego vehicle, or (b) filtering the determined lane overtime. For example, a Kalman filter or extended Kalman filter (EKF) maybe used to account for previous lane determination results. In this way,a confidence level may be provided for cross checking the resultprovided by the processed radar data. In embodiments, the step ofdetermining the plausibility further comprises filtering outdeterminations having a low probability.

According to a second aspect, there is provided a computer programproduct including instructions which, when the program is executed by acomputer, cause the computer to carry out the steps of the above method.In this way, a computer program product is provided for implementing theabove method.

According to a third aspect, there is provided a non-transitory computerreadable medium including instructions, which when executed by aprocessor, cause the processor to execute the above method. In this way,a non-transitory computer readable medium is provided for implementingthe above method.

According to a fourth aspect, there is provided a processing apparatusimplementing the above method.

According to a fifth aspect, there is provided a vehicle comprising: anAdvanced Driver Assistance System, ADAS, for implementing one or morefunctions; and the above processing apparatus for inputting a determinedlane of the vehicle to the ADAS for use during the one or morefunctions.

BRIEF DESCRIPTION OF DRAWINGS

Illustrative embodiments will now be described with reference to theaccompanying drawings in which:

FIGS. 1 a to 1 d are a flow diagram of a vehicle lane determinationmethod according to a first embodiment;

FIG. 2 is a schematic illustration of a processing apparatus used forlane determination according to the first embodiment;

FIG. 3 is a schematic illustration of a vehicle lane determinationmethod according to a second embodiment;

FIGS. 4 a to 4 b show illustrative histograms used in the secondembodiment;

FIG. 5 is a schematic illustration of the second embodiment when appliedto a curved road;

FIG. 6 is a schematic illustration of a vehicle lane determinationmethod according to a third embodiment;

FIGS. 7 a to 7 b show illustrative histograms used in the thirdembodiment;

FIG. 8 is a schematic illustration of a vehicle lane determinationmethod according to a fourth embodiment; and

FIGS. 9 a and 9 b show illustrative histograms used in the fourthembodiment.

DETAILED DESCRIPTION

Embodiments of the invention utilise radar detections determined fromradar data moving in the same longitudinal direction as ego vehicle andreflected back from surrounding vehicles to determine the lane-number ofthe ego vehicle. In this respect, FIG. 1 is a flow diagram of a vehiclelane determination method according to a first embodiment, and FIG. 2 isa schematic illustration of a processing apparatus for implementing thefirst embodiment.

In step 1, radar data is received at a input 101. As shown in the insert(a) in FIG. 1 , each received radar detection 11 is associated with aposition in the vehicle coordinate system (VCS) relative to the egovehicle 10.

In step 2, the detections 11 are filtered through a filtering block 102to remove non-relevant detections 11 b associated with non-movingobjects or objects moving in an opposite direction. For example, in theinsert (a) in FIG. 1 , a high density of unwanted detections 11 b arereceived from vehicles in the adjacent roadway travelling in theopposite direction. These unwanted detections may be filtered out basedon, for example, their range rate or their tracked trails, to provide afiltered output of relevant detections 11 a mapped within the VCS, asshown in the insert (b) in FIG. 1 .

This filtered output is then processed through a clustering block 103,which clusters the relevant detections 11 a into clusters 12 associatedwith objects with respect to their lateral offset within the VCS. Forillustration, this is shown graphically in the insert (c) in FIG. 1 . Inembodiments, a K-means clustering methodology is used. In thisembodiment, the clustering block 103 also projects the movement of theclustered detections, and hence the associated objects, integrating inknown information on the number of lanes derived from standard or highdefinition map data. The clustering block 103 may also receive yawsensor data from within the vehicle to thereby identify bends in theroadway and account for this in the clustering process.

In step 4, the clustered data is processed by a determination block 104for determining the lane of the ego vehicle 10. For this, the detectionclusters 12 are counted as neighbouring vehicles in at least one offsetdirection 13 (e.g. to the left of the ego vehicle). That is, thelane-number of the ego vehicle may be determined by adding one to thenumber of clusters identified to the left (or right) of the ego vehicle10. For example, in the insert (d) in FIG. 1 , two clusters 12 aredetected, indicating the ego vehicle 10 is in the third lane. In otherembodiments, the clusters in both lateral directions may be used incombination to determine the lane-number.

In step 4, the determined vehicle lane may be processed by aplausibility block 105 to filter or smooth erroneous determinations. Inembodiments, this may be implemented based on the known number of lanesprovided by map data. The plausibility block 105 may also track the lanenumber of the ego vehicle over time and integrate this using, forexample, Markov-Models to improve the robustness of the output result 6.

A second embodiment will now be described in relation to FIGS. 3 to 5 .This embodiment is very similar to the first embodiment and hence willonly be described briefly. As shown in FIG. 3 , radar detections areagain filtered to remove static detections and detections that are notmoving in the ego direction. Detections 11 along a selected roadcoordinate system 17 are then represented onto a lateral offsetdirection, taking the road curvature into account.

In this connection, a road coordinate system 17 contains both semanticinformation and positional information about the road. Semanticinformation can be for example the type of the road (motorway, urbanstreet, etc.), the shape of the road (straight, curve, intersection,etc.), the width of the road, the number and the width of lanes, theboundary type of the road (guardrails, curb stones, etc.), the roadlayout (structure of lanes like splits and merges), the road curvatureand other such road characteristic information. The positionalinformation can be for example lane-level information (coordinates oflanes, lane-markers and virtual centre-lines), coordinates of roadboundaries, coordinates of objects like signs and many more. Inembodiments, the semantic and positional information can be derived froma map (e.g. HD-Map) or can be derived from sensor measurements. Forexample by detecting the guardrails, the road width can be determinedand from the road width a maximal number of lanes can be derived. Alsothe shape of the road or the road curvature can be derived from sensormeasurements. In other words, the semantic information can be seen as“coarse” information, while the positional information is effectively“fine” information with coordinates.

The road coordinate system 17, especially the semantic attributes can beselected by selecting its type (e.g. motorway) and/or its shape (e.g.curve with specific curvature). Selection can also take into account theposition of the ego-vehicle. For example, if a road coordinate system isderived from a map, the location of the vehicle is used to select thecorrect road coordinate system current road segment from the map andhence thus the need to be selected.

Furthermore, a road characteristic is a single element of the semanticinformation of the road coordinate system. For example, the shape of theroad or the road width are a characteristic.

The selection of the road coordinate system 17 may be based on map datain combination with GNSS/positional data, as well as other sensormeasurements. For example, the unfiltered radar data received at input101 may be used, at least in part, to determine the selection of theroad coordinate system.

Accordingly, the selected road coordinate system 17 provides informationon the both the current and upcoming road curvature/shape derived, forinstance, from map data and/or estimated from a detected yaw rate forthe ego vehicle and other sensor data. As such, whether the road isstraight, as shown in FIG. 3 , or curved, as shown in FIG. 5 , a similarhistogram of relevant radar detections 11 a against lateral offset 13may be produced, as shown in FIG. 4 a . It will be understood that thehistogram is shown for illustration and it may not be created inpractice. As described above in relation to FIG. 1 , the clusters ofrelevant detections 11 a may then be counted as shown in FIG. 4(b),taking into account the minimum spacing dictated by the lane markings14. The lane number of the ego-vehicle may then be determined.

A third embodiment will now be described in relation to FIGS. 6 and 7 .This embodiment is again similar to the first and second embodiments,but clusters of detections 11 are identified as objects 12. The drivingdirection of the objects 12 is then tracked, and the number of laterallyoffset objects is counted, again accounting for a minimum lateralspacing distance between them. As shown in FIG. 7 a , three objects aredetected, but as shown in FIG. 7 b , the clustering process identifiesonly two lanes are present because the two detections at position “1”are too close together to relate to separate lanes. The ego vehicle isthen identified as being in lane three.

A fourth embodiment will now be described in relation to FIGS. 8 and 9 .This embodiment is similar to the third embodiment, but makes use ofadditional information provided by a high definition map in selectingthe road coordinate system 17. In this case, the clustering block 103may take into account known distances between the lanes, as well as thedistances between the road's guardrails/barriers 15. As shown in FIG.9(b), this may provide more precise minimum distance parameters 16 fordifferentiating objects along the lateral offset.

Accordingly, with the above arrangements, a radar-based system isprovided for lane determination, which utilises detections from othervehicles and objects to identify the current lane of the ego vehicle. Assuch, characteristics from the radar data associated with othervehicles, such as cluster detections, may thereby be used to determinethe ego lane. Advantageously, this avoids the need for expensiverepainting of road markings or the provision of guiderails. Equally, thelimitations of camera-based systems, such as low reliability in poorweather or at night, are avoided. The radar data may also be processedin conjunction with location and standard or high definition map data toprovide improved accuracy and/or plausibility determination.

It will be understood that the embodiments illustrated above showapplications only for the purposes of illustration. In practice,embodiments may be applied to many different configurations, thedetailed embodiments being straightforward for those skilled in the artto implement.

For example, it will be understood that, whilst the apparatus describedin FIG. 2 shows a plurality of separate blocks, these processingfunctions may be implemented using one or more microprocessors, such aswithin a vehicle electronic control unit (ECU).

1. A vehicle lane determination method comprising the steps of:determining radar data acquired at an ego vehicle; extractingcharacteristics from the radar data associated with other vehicles,wherein the extracted characteristics comprise positional informationfor the other vehicles relative to the ego vehicle, and wherein the stepof extracting comprises selecting a road coordinate system and thepositional information comprises a representation in the selected roadcoordinate system; and determining the lane of the ego vehicle based onthe extracted characteristics meeting a set of lane determinationconditions.
 2. A vehicle lane determination method according to claim 1,wherein the step of extracting characteristics from the determined radardata comprises identifying other vehicles moving in a same longitudinaldirection to the ego vehicle.
 3. A vehicle lane determination methodaccording to claim 1, wherein the positional information comprises arepresentation of the other vehicles in a lateral direction relative tothe ego vehicle.
 4. A vehicle lane determination method according toclaim 1, wherein the selected road coordinate system is selected toaccount for a location of the ego vehicle.
 5. A vehicle lanedetermination method according to claim 1, wherein the selected roadcoordinate system is selected to account for the current roadcharacteristics.
 6. A vehicle lane determination method according toclaim 1, wherein the extracted characteristics from the radar datafurther comprise at least one of the speed, heading, yaw rate,orientation, acceleration, positions, and size of the other vehicles. 7.A vehicle lane determination method according to claim 1, wherein thestep of determining the lane of the ego vehicle further compriseaccounting for at least one of the speed, heading, yaw rate,orientation, acceleration and size of the ego vehicle.
 8. A vehicle lanedetermination method according to claim 1, wherein the step ofextracting characteristics comprises accounting for a determined minimumlateral distance between other vehicles.
 9. A vehicle lane determinationmethod according to claim 1, wherein the step of determining the lane ofthe ego vehicle comprises using data from the selected road coordinatesystem.
 10. A vehicle lane determination method according to claim 1,wherein the step of determining the lane of the ego vehicle comprisesaccounting for a known number of lanes for the location of the egovehicle determined from road data.
 11. A vehicle lane determinationmethod according to claim 1, further comprising the step of verifying aplausibility for the determined lane of the ego vehicle based on atleast one of: (a) a known number of lanes for the location of the egovehicle, or (b) filtering the determined lane over time.
 12. A computerprogram product comprising instructions which, when the program isexecuted by a computer, cause the computer to carry out the steps of themethod according to claim
 1. 13. A non-transitory computer readablemedium comprising instructions, which when executed by a processor,cause the processor to execute the method according to claim
 1. 14. Avehicle comprising: an Advanced Driver Assistance System, ADAS, forimplementing one or more functions; and a processing apparatusconfigured for implementing the method according to claim 1 to input adetermined lane of the vehicle to the ADAS for use during the one ormore functions.